Data-Driven Covariance Steering with Output Feedback

TL;DR

This paper develops a data-driven approach for output-feedback covariance steering in stochastic linear systems without knowing the system model, using offline data and convex optimization.

Contribution

It introduces a novel data-driven framework that estimates system components and solves covariance steering via convex relaxation without requiring a system model.

Findings

The proposed method effectively handles temporally correlated noise.

Numerical simulations demonstrate the framework's performance.

The approach enables covariance steering using only offline data.

Abstract

This paper addresses the problem of output-feedback covariance steering for stochastic, discrete-time, linear, time-invariant systems without knowledge of the system model. We employ a controllable, non-minimal state representation constructed from past inputs and outputs and convert the problem to one in state-feedback form. In this representation, the induced disturbance becomes temporally correlated, which requires explicit propagation of the cross-covariance between the state and disturbance processes. To handle the lack of a system model, we leverage persistently exciting data collected offline and formulate the mean and covariance steering problems using an indirect and a direct approach, respectively. The indirect formulation requires an estimate of the mean dynamics model, while the direct formulation relies on an estimate of the noise realization in the collected data. To this end, we present an estimation method suitable to handle temporally correlated noise, enabling consistent identification of both components. Using a convex relaxation, we convert the covariance steering problem to a semidefinite program that can be solved efficiently. We conduct numerical simulations to evaluate the performance of the developed framework.